An intriguing discrepancy exists between the performance deficits traditionally observed in the laboratory and the general competencies that older adults exhibit in everyday life. Parallel to the general public's two conflicting views about aging-that age brings wisdom, and that age brings deteriorating skills-research has found mixed evidence showing that older adults' decisions can be both wiser and less wise compared to their younger counterparts. Convergent evidence suggests that emotional regulation, reasoning about interpersonal conflicts, and crystallized intelligence all improve with advanced age. However, older adults also suffer from declines in the cognitive abilities necessary for processing new information, including reasoning, dividing attention, processing speed, and working memory-i.e., what is known as fluid intelligence. Whereas the effects of declines in fluid intelligence on decisions, particularly novel ones, are well understood, the proposed research will examine what components of crystallized intelligence-the part of general intelligence that involves knowledge, experience, and expertise-contribute to wisdom in decision making in older adults. This application has two specific goals. The first is to clarify the relationship between crystallized intelligence and decision making. Previous work has shown that better crystallized intelligence may compensate for worse fluid intelligence in older adults, but we know little about what abilities the tests of general knowledge and vocabulary used to measures crystallized intelligence actually capture and how these domain-general abilities contribute to decision quality across domains. For example, domain-general knowledge may correlate with domain-specific (i.e., semantic) knowledge, or it may proxy for a number of other abilities that improve with age, including domain-specific process expertise (i.e., automatic processing ability gained from practice), domain-general principles (e.g., knowing to seek advice, eliminating dominated alternatives, and identifying redundant information), and emotion-based aspects of decision making (such as reliance on affective sources of information). The second goal is to explore the possible connection between crystallized intelligence and an important, specific kind of decision skill, affective forecasting, or the ability to predict what will make us happy. Affective variable play a major role in decision processes, and age differences in emotional aspects of decision-making would seem to have important implications for choice outcomes. If older adults make decisions that deviate from what is economically rational, they may nonetheless be happier with their decisions because they are better at predicting how they will feel about the outcomes. Individual differences in affective forecasting accuracy may also be influenced by other abilities that change with age, including components of both fluid and crystallized intelligence. We examine decisions that have implications for the welfare of older adults, across a wide range of domains, including retirement and healthcare. Research insights gained will provide directions for the development of interventions that can aid and improve decisions-an important contribution in an aging society when major decisions (e.g., retirement accumulation and spending and health care choices) are increasingly up to individuals. PUBLIC HEALTH RELEVANCE: By understanding what components of individual and lifespan differences in crystallized intelligence lead adults across the lifespan to make better decisions-both economically and emotionally-across a wide range of domains (including retirement and healthcare), the proposed research provides directions for the development of interventions that can aid decisions and improve the welfare of older adults. These psychological and behavioral economic interventions offer an important contribution in an aging society that is increasingly shifting socially and individually important decisions (e.g., on retirement saving and decumulation and on health care choices) from government agencies to individuals. The tenuous evidence for the effectiveness of fluid intelligence training (Owen et al., 2010) makes it important to find alternative avenues to improve decision quality by designing interventions that capitalize on components of crystallized intelligence that influence competencies relevant to decision and choice.